Given an ordered sample of either exceedances or upper order statistics which is to be modeled using a GPD, this
function provides Falk's estimator of the shape parameter *γ \in [-1,0]*. Precisely,

*\hat γ_{\rm{Falk}} = \hat γ_{\rm{Falk}}(k, n) = \frac{1}{k-1} ∑_{j=2}^k \log \Bigl(\frac{X_{(n)}-H^{-1}((n-j+1)/n)}{X_{(n)}-H^{-1}((n-k)/n)} \Bigr), \; \; k=3, … ,n-1*

for $H$ either the empirical or the distribution function based on the log–concave density estimator.
Note that for any *k*, *\hat γ_{\rm{Falk}} : R^n \to (-∞, 0)*. If
*\hat γ_{\rm{Falk}} \not \in [-1,0)*, then it is likely that the log-concavity assumption is violated.

1 |

`est` |
Log-concave density estimate based on the sample as output by |

`ks` |
Indices |

n x 3 matrix with columns: indices *k*, Falk's estimator based on the log-concave density estimate, and
the ordinary Falk's estimator based on the order statistics.

Kaspar Rufibach (maintainer), kaspar.rufibach@gmail.com,

http://www.kasparrufibach.ch

Samuel Mueller, samuel.mueller@sydney.edu.au,

www.maths.usyd.edu.au/ut/people?who=S_Mueller

Kaspar Rufibach acknowledges support by the Swiss National Science Foundation SNF, http://www.snf.ch

Mueller, S. and Rufibach K. (2009).
Smooth tail index estimation.
*J. Stat. Comput. Simul.*, **79**, 1155–1167.

Falk, M. (1995).
Some best parameter estimates for distributions with finite endpoint.
*Statistics*, **27**, 115–125.

Other approaches to estimate *γ* based on the fact that the density is log–concave, thus
*γ \in [-1,0]*, are available as the functions `pickands`

, `falkMVUE`

, `generalizedPick`

.

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